BJR open最新文献

{"title":"\"Evolving role of AI in radiation oncology\"- special collection - introductory Editorial.","authors":"Sarah Mattonen, Issam El Naqa, Weigang Hu, Esther Troost","doi":"10.1259/bjro.20229002","DOIUrl":"10.1259/bjro.20229002","url":null,"abstract":"","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20229002"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10958999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47606590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole-body MRI in children: state of the art.","authors":"Trevor Gaunt, Paul D Humphries","doi":"10.1259/bjro.20210087","DOIUrl":"10.1259/bjro.20210087","url":null,"abstract":"<p><p>Whole-body magnetic resonance imaging (WBMRI) is an increasingly popular technique in paediatric imaging. It provides high-resolution anatomical information, with the potential for further exciting developments in acquisition of functional data with advanced MR sequences and hybrid imaging with radionuclide tracers. WBMRI demonstrates the extent of disease in a range of multisystem conditions and, in some cases, disease burden prior to the onset of clinical features. The current applications of WBMRI in children are hereby reviewed, along with suggested anatomical stations and sequence protocols for acquisition.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20210087"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10958622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44827349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding our concept of simulation in radiology: a \"Radiology Requesting\" session for undergraduate medical students.","authors":"James Hartley, Bobby Agrawal, Karamveer Narang, Edel Kelliher, Elizabeth Lunn, Roshni Bhudia","doi":"10.1259/bjro.20220012","DOIUrl":"10.1259/bjro.20220012","url":null,"abstract":"<p><strong>Objectives: </strong>Whilst radiology is central to the modern practice of medicine, graduating doctors often feel unprepared for radiology in practice. Traditional radiological education focuses on image interpretation. Key areas which are undertaught include communication skills relating to the radiology department. We sought to design teaching to fill this important gap.</p><p><strong>Methods: </strong>We developed a small group session using <i>in situ</i> simulation to enable final and penultimate year medical students to develop radiology-related communication and reasoning skills. Students were given realistic cases, and then challenged to gather further information and decide on appropriate radiology before having the opportunity to call a consultant radiologist on a hospital phone and simulate requesting the appropriate imaging with high fidelity. We evaluated the impact of the teaching through before-and-after Likert scales asking students about their confidence with various aspects of requesting imaging, and qualitatively through open-ended short answer questionnaires.</p><p><strong>Results: </strong>The session was delivered to 99 students over 24 sessions. Self-reported confidence in discussing imaging increased from an average of 1.7/5 to 3.4/5 as a result of the teaching (<i>p</i> < 0.001) and students perceived that they had developed key skills in identifying and communicating relevant information.</p><p><strong>Conclusions: </strong>The success of this innovative session suggests that it could form a key part of future undergraduate radiology education, and that the method could be applied in other areas to broaden the application of simulation.</p><p><strong>Advances in knowledge: </strong>This study highlights a gap in undergraduate medical education. It describes and demonstrates the effectiveness of an intervention to fill this gap.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20220012"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10958647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48493377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RRIMS: Radiation Risk In Mammography Screening - a novel model for predicting the lifetime dose and risk of radiation-induced breast cancer from the first screening visit.","authors":"Sahand Hooshmand, Warren M Reed, Mo'ayyad E Suleiman, Patrick C Brennan","doi":"10.1259/bjro.20220028","DOIUrl":"10.1259/bjro.20220028","url":null,"abstract":"<p><strong>Objectives: </strong><b>R</b>adiation <b>R</b>isk <b>I</b>n <b>M</b>ammography <b>S</b>creening (<b>RRIMS</b>) builds on the prototype, formerly known as Breast-iRRISC, to develop a model that aims to establish a dose and risk profile for females by calculating their lifetime mean glandular dose (MGD) for each age of screening between 40 and 75 years, using only the information from her first screening visit. This is then used to allocate her to a dose category and estimate the lifetime risk of radiation-induced breast cancer incidence and mortality for a population of females in that category.</p><p><strong>Methods: </strong>This model training was developed using a large dataset of Hologic images containing a total of 20,232 images from 5,076 visits from 4,154 females. The female's breast characteristics and exposure parameters were extracted from the images to calculate the female's MGD throughout a lifetime of screening from just her first screening visit, using modelling of various parameters and their change through time.</p><p><strong>Results: </strong>This development has ultimately provided a model that uses the female's first screening visit to calculate the received MGD for all ages of potential screening. This has enabled the allocation of females to either a low-, medium-, or high-dose category, ultimately followed by the lifetime effective risk (LER) estimation for any screening attendance pattern. A female in the low-dose category undergoing biennial screening from 50 to 74 years would expect a risk of radiation-induced breast cancer incidence and mortality of 8.64 and 2.61 cases per 100,000 females, respectively. Similarly, a female in the medium- or high-dose category undergoing the same regimen would expect an incidence and mortality risk of 11.76 and 3.55, and 15.08 and 4.55 cases per 100,000 females, respectively.</p><p><strong>Conclusions: </strong>This novel approach of establishing a female's dose profile and lifetime risk from a single visit will further assist females in their informed consent on breast screening attendance and help inform policy-makers when exploring the benefits and drawbacks of various screening patterns and frequencies.</p><p><strong>Advances in knowledge: </strong>RRIMS is a novel tool that enables the assessment of a female's lifetime dose and risk profile using only the information from her first screening visit.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20220028"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10958679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49268188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers.","authors":"Shangyuan Ye,&nbsp;Jeong Youn Lim,&nbsp;Wei Huang","doi":"10.1259/bjro.20210083","DOIUrl":"10.1259/bjro.20210083","url":null,"abstract":"<p><p>Quantitative imaging biomarkers (QIBs) are increasingly used in clinical studies. Because many QIBs are derived through multiple steps in image data acquisition and data analysis, QIB measurements can produce large variabilities, posing a significant challenge in translating QIBs into clinical trials, and ultimately, clinical practice. Both repeatability and reproducibility constitute the reliability of a QIB measurement. In this article, we review the statistical aspects of repeatability and reproducibility of QIB measurements by introducing methods and metrics for assessments of QIB repeatability and reproducibility and illustrating the impact of QIB measurement error on sample size and statistical power calculations, as well as predictive performance with a QIB as a predictive biomarker.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20210083"},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40712920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strengths and challenges of the artificial intelligence in the assessment of dense breasts.","authors":"Sahar Mansour, Somia Soliman, Abisha Kansakar, Ahmed Marey, Christiane Hunold, Mennatallah Mohamed Hanafy","doi":"10.1259/bjro.20220018","DOIUrl":"10.1259/bjro.20220018","url":null,"abstract":"<p><strong>Objectives: </strong>High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts.</p><p><strong>Methods: </strong>This study included 6600 mammograms of dense patterns \"c\" and \"d\" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG).</p><p><strong>Results: </strong>Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms.</p><p><strong>Conclusion: </strong>Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer.</p><p><strong>Advances in knowledge: </strong>Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20220018"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10958665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46465845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What morphological MRI features enable differentiation of low-grade from high-grade soft tissue sarcoma?","authors":"Sana Boudabbous,&nbsp;Marion Hamard,&nbsp;Essia Saiji,&nbsp;Karel Gorican,&nbsp;Pierre-Alexandre Poletti,&nbsp;Minerva Becker,&nbsp;Angeliki Neroladaki","doi":"10.1259/bjro.20210081","DOIUrl":"https://doi.org/10.1259/bjro.20210081","url":null,"abstract":"<p><strong>Objective: </strong>To assess the diagnostic performance of morphological MRI features separately and in combination for distinguishing low- from high-grade soft tissue sarcoma (STS).</p><p><strong>Methods and materials: </strong>We retrospectively analysed pre-treatment MRI examinations with T1, T2 with and without fat suppression (FS) and contrast-enhanced T1 obtained in 64 patients with STS categorized histologically as low (<i>n</i> = 21) versus high grade (<i>n</i> = 43). Two musculoskeletal radiologists blinded to histology evaluated MRI features. Diagnostic performance was calculated for each reader and for MRI features showing significant association with histology (<i>p</i> < 0.05). Logistic regression analysis was performed to develop a diagnostic model to identify high-grade STS.</p><p><strong>Results: </strong>Among all evaluated MRI features, only six features had adequate interobserver reproducibility (kappa>0.5). Multivariate logistic regression analysis revealed a significant association with tumour grade for lesion heterogeneity on FS images, intratumoural enhancement≥51% of tumour volume and peritumoural enhancement for both readers (<i>p</i> < 0.05). For both readers, the presence of each of the three features yielded odds ratios for high grade versus low grade from 4.4 to 9.1 (<i>p</i> < 0.05). The sum of the positive features for each reader independent of reader expertise yielded areas under the curve (AUCs) > 0.8. The presence of ≥2 positive features indicated a high risk for high-grade sarcoma, whereas ≤1 positive feature indicated a low-to-moderate risk.</p><p><strong>Conclusion: </strong>A diagnostic MRI score based on tumour heterogeneity, intratumoural and peritumoural enhancement enables identification of lesions that are likely to be high-grade as opposed to low-grade STS.</p><p><strong>Advances in knowledge: </strong>Tumour heterogeneity in Fat Suppression sequence, intratumoural and peritumoural enhancement is identified as signs of high-grade sarcoma.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20210081"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40357659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic.","authors":"Dana AlNuaimi, Reem AlKetbi","doi":"10.1259/bjro.20210075","DOIUrl":"10.1259/bjro.20210075","url":null,"abstract":"<p><p>Artificial intelligence (AI) plays a crucial role in the future development of all healthcare sectors ranging from clinical assistance of physicians by providing accurate diagnosis, prognosis and treatment to the development of vaccinations and aiding in the combat against the Covid-19 global pandemic. AI has an important role in diagnostic radiology where the algorithms can be trained by large datasets to accurately provide a timely diagnosis of the radiological images given. This has led to the development of several AI algorithms that can be used in regions of scarcity of radiologists during the current pandemic by simply denoting the presence or absence of Covid-19 pneumonia in PCR positive patients on plain chest radiographs as well as in helping to levitate the over-burdened radiology departments by accelerating the time for report delivery. Plain chest radiography is the most common radiological study in the emergency department setting and is readily available, fast and a cheap method that can be used in triaging patients as well as being portable in the medical wards and can be used as the initial radiological examination in Covid-19 positive patients to detect pneumonic changes. Numerous studies have been done comparing several AI algorithms to that of experienced thoracic radiologists in plain chest radiograph reports measuring accuracy of each in Covid-19 patients. The majority of studies have reported performance equal or higher to that of the well-experienced thoracic radiologist in predicting the presence or absence of Covid-19 pneumonic changes in the provided chest radiographs.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"4 1","pages":"20210075"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9374861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning in breast imaging.","authors":"Arka Bhowmik, Sarah Eskreis-Winkler","doi":"10.1259/bjro.20210060","DOIUrl":"10.1259/bjro.20210060","url":null,"abstract":"<p><p>Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"4 1","pages":"20210060"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10829285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetization transfer imaging of ovarian cancer: initial experiences of correlation with tissue cellularity and changes following neoadjuvant chemotherapy.","authors":"Surrin S Deen, Mary A McLean, Andrew B Gill, Robin A F Crawford, John Latimer, Peter Baldwin, Helena M Earl, Christine A Parkinson, Sarah Smith, Charlotte Hodgkin, Mercedes Jimenez-Linan, Cara R Brodie, Ilse Patterson, Helen C Addley, Susan J Freeman, Penelope M Moyle, Martin J Graves, Evis Sala, James D Brenton, Ferdia A Gallagher","doi":"10.1259/bjro.20210078","DOIUrl":"10.1259/bjro.20210078","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the relationship between magnetization transfer (MT) imaging and tissue macromolecules in high-grade serous ovarian cancer (HGSOC) and whether MT ratio (MTR) changes following neoadjuvant chemotherapy (NACT).</p><p><strong>Methods: </strong>This was a prospective observational study. 12 HGSOC patients were imaged before treatment. MTR was compared to quantified tissue histology and immunohistochemistry. For a subset of patients (<i>n</i> = 5), MT imaging was repeated after NACT. The Shapiro-Wilk test was used to assess for normality of data and Spearman's rank-order or Pearson's correlation tests were then used to compare MTR with tissue quantifications. The Wilcoxon signed-rank test was used to assess for changes in MTR after treatment.</p><p><strong>Results: </strong>Treatment-naïve tumour MTR was 21.9 ± 3.1% (mean ± S.D.). MTR had a positive correlation with cellularity, rho = 0.56 (<i>p</i> < 0.05) and a negative correlation with tumour volume, ρ = -0.72 (<i>p</i> = 0.01). MTR did not correlate with the extracellular proteins, collagen IV or laminin (<i>p</i> = 0.40 and <i>p</i> = 0.90). For those patients imaged before and after NACT, an increase in MTR was observed in each case with mean MTR 20.6 ± 3.1% (median 21.1) pre-treatment and 25.6 ± 3.4% (median 26.5) post-treatment (<i>p</i> = 0.06).</p><p><strong>Conclusion: </strong>In treatment-naïve HGSOC, MTR is associated with cellularity, possibly reflecting intracellular macromolecular concentration. MT may also detect the HGSOC response to NACT, however larger studies are required to validate this finding.</p><p><strong>Advances in knowledge: </strong>MTR in HGSOC is influenced by cellularity. This may be applied to assess for cell changes following treatment.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"4 1","pages":"20210078"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9374864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}